
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.metrics import accuracy_score
import re
from scipy.special import expit  # Sigmoid function
np.seterr(divide='ignore', invalid='ignore')

{'divide': 'ignore', 'over': 'warn', 'under': 'ignore', 'invalid': 'ignore'}


data = pd.read_csv('globa_data.csv')


data


# Load the dataset (replace 'your_data.csv' with the actual file name or source)
data = pd.read_csv('globa_data.csv')

# Define a function to clean and convert to float
def clean_and_convert(value):
    if isinstance(value, str):
        cleaned_value = re.sub(r'[^\d.]', '', value)
        if cleaned_value:
            return float(cleaned_value)
    return value

# Apply the cleaning and conversion function to all columns
for column in data.columns:
    data[column] = data[column].apply(clean_and_convert)

# Encode the 'Entity' column using LabelEncoder

# data['Entity'] = label_encoder.fit_transform(data['Entity'])

# Define the target variable and classes
data['Access Level'] = pd.cut(data['Access to electricity (% of population)'],
                              bins=[0, 20, 40, 60, 80, 100],
                              labels=['Very Low', 'Low', 'Moderate', 'High', 'Very High'])


data["Access Level"].dropna(inplace=True)  

#dropping columns that are not relevant to the analysis
data.drop(columns=['Access to electricity (% of population)'], inplace=True)
data = data.drop(columns=["Renewable energy share in the total final energy consumption (%)"])
data = data.drop(columns=["Renewables (% equivalent primary energy)"])
data = data.drop(columns=["Financial flows to developing countries (US $)"])
data = data.drop(columns=["Renewable-electricity-generating-capacity-per-capita"])
data = data.drop(columns=["Longitude"])
data = data.drop(columns=["Latitude"])
data = data.drop(columns=["Land Area(Km2)"])
isna_mask = data.isna().sum(axis=1) > 0
# print(isna_mask.sum())
data = data[~isna_mask]


y = data["Access Level"].reset_index(drop=True)
# Split the data into features (X) and target (y)
X = data.drop(columns=['Access Level'])



# One-hot encode the categorical 'Entity' column
X = pd.get_dummies(X, columns=['Entity'], prefix=['Entity'])


# Perform feature scaling on the X data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)



#train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)


label_encoder = LabelEncoder()
label_encoder.fit(y_train)
y_train = label_encoder.transform(y_train)
y_test = label_encoder.transform(y_test)


X_new = pd.DataFrame(X_scaled, columns = X.columns) 
X_new.describe()


%%time
# from last time, our logistic regression algorithm is given by (including everything we previously had):
class BinaryLogisticRegression:
    def __init__(self, eta, iterations=20, C1=0.001, C2=0.001):
        self.eta = eta
        self.iters = iterations
        self.C1 = C1
        self.C2 = C2
        # internally we will store the weights as self.w_ to keep with sklearn conventions

    def __str__(self):
        if(hasattr(self,'w_')):
            return 'Binary Logistic Regression Object with coefficients:\n'+ str(self.w_) # is we have trained the object
        else:
            return 'Untrained Binary Logistic Regression Object'

    # convenience, private:
    @staticmethod
    def _add_bias(X):
        return np.hstack((np.ones((X.shape[0],1)),X)) # add bias term

    @staticmethod
    def _sigmoid(theta):
        # increase stability, redefine sigmoid operation
        return expit(theta) #1/(1+np.exp(-theta))

    # public:
    def predict_proba(self,X,add_bias=True):
        # add bias term if requested
        Xb = self._add_bias(X) if add_bias else X
        return self._sigmoid(Xb @ self.w_) # return the probability y=1

    def predict(self,X):
        return (self.predict_proba(X)>0.5) #return the actual prediction

    # vectorized gradient calculation with regularization using L2 Norm
    def _get_gradient(self,X,y):
        ydiff = y-self.predict_proba(X,add_bias=False).ravel() # get y difference
        gradient = np.mean(X * ydiff[:,np.newaxis], axis=0) # make ydiff a column vector and multiply through

        gradient = gradient.reshape(self.w_.shape)
        #gradient[1:] += -2 * self.w_[1:] * self.C1 # TODO REMOVE THIS
        return gradient
    def _get_gradient_L2(self, X, y):
        gradient = self._get_gradient(X, y)
        gradient[1:] += -2 * self.w_[1:] * self.C2
        return gradient
    def _get_gradient_L1(self, X, y):
        gradient = self._get_gradient(X, y)
        l1_der = self.w_[1:] / np.abs(self.w_[1:])
        l1_der[self.w_[1:] == 0] = 0
        gradient[1:] +=  -1 * l1_der * self.C1
        return gradient
    def _get_gradient_elastic(self, X, y):
        gradient = self._get_gradient(X, y)
        l1_der = self.w_[1:] / np.abs(self.w_[1:])
        l1_der[self.w_[1:] == 0] = 0
        gradient[1:] +=  -1 * l1_der * self.C1
        gradient[1:] += -2 * self.w_[1:] * self.C2
        return gradient


    def fit(self, X, y, regularization=None):
        Xb = self._add_bias(X) # add bias term
        num_samples, num_features = Xb.shape

        self.w_ = np.zeros((num_features,1)) # init weight vector to zeros

        # for as many as the max iterations
        for _ in range(self.iters):
            if(regularization == 'L1'):
                gradient = self._get_gradient_L1(Xb, y)
            elif(regularization == 'L2'):
                gradient = self._get_gradient_L2(Xb, y)
            elif(regularization == 'elastic'):
                gradient = self._get_gradient_elastic(Xb, y)
            else:
                gradient = self._get_gradient(Xb,y)
            self.w_ += gradient*self.eta # multiply by learning rate
            # add bacause maximizing

blr = BinaryLogisticRegression(eta=0.1,iterations=50,C1=0.001)

blr.fit(X_train,y_train,regularization=None)
yhat = blr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.10553633217993079
CPU times: user 83 ms, sys: 137 ms, total: 220 ms
Wall time: 33.2 ms


class StochasticLogisticRegression(BinaryLogisticRegression):
    # stochastic gradient calculation
    def _get_gradient(self,X,y):

        # grab a subset of samples in a mini-batch
        # and calculate the gradient according to the small batch only
        mini_batch_size = 16
        idxs = np.random.choice(len(y), mini_batch_size)

        ydiff = y[idxs]-self.predict_proba(X[idxs],add_bias=False).ravel() # get y difference (now scalar)
        gradient = np.mean(X[idxs] * ydiff[:,np.newaxis], axis=0) # make ydiff a column vector and multiply through

        gradient = gradient.reshape(self.w_.shape)
        #gradient[1:] += -2 * self.w_[1:] * self.C2

        return gradient


slr = StochasticLogisticRegression(eta=0.01, iterations=300,
                                   C1=0.001,
                                   C2=0.001) # take a lot more steps!!

slr.fit(X_train,y_train,regularization=None)

yhat = slr.predict(X_test)

print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.10899653979238755


class VectorBinaryLogisticRegression(BinaryLogisticRegression):
    # inherit from our previous class to get same functionality
    @staticmethod
    def _sigmoid(theta):
        # increase stability, redefine sigmoid operation
        return expit(theta) #1/(1+np.exp(-theta))

    # but overwrite the gradient calculation
    def _get_gradient(self,X,y):
        ydiff = y-self.predict_proba(X,add_bias=False).ravel() # get y difference
        gradient = np.mean(X * ydiff[:,np.newaxis], axis=0) # make ydiff a column vector and multiply through

        return gradient.reshape(self.w_.shape)

    def _get_gradient_L1(self, X, y):
        gradient = self._get_gradient(X, y)
        l1_der = self.w_[1:] / np.abs(self.w_[1:])
        l1_der[self.w_[1:] == 0] = 0
        gradient[1:] +=  -1 * l1_der * self.C1
        return gradient

    def _get_gradient_L2(self, X, y):
        gradient = self._get_gradient(X, y)
        gradient[1:] += -2 * self.w_[1:] * self.C2
        return gradient



vlr = VectorBinaryLogisticRegression(eta=0.01, iterations=300,
                                     C1=0.001,
                                     C2=0.001) # take a lot more steps!!

vlr.fit(X_train,y_train,regularization=None)

yhat = vlr.predict(X_test)

print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.11072664359861592


blr = BinaryLogisticRegression(eta = 0.001, iterations=100)
blr.fit(X_train, y_train)
y_pred = blr.predict(X_test)

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

Accuracy: 0.10207612456747404


%%time
# for this, we won't perform our own BFGS implementation
# (it takes a fair amount of code and understanding, which we haven't setup yet)
# luckily for us, scipy has its own BFGS implementation:
from scipy.optimize import fmin_bfgs # maybe the most common bfgs algorithm in the world
from numpy import ma
class BFGSBinaryLogisticRegression(BinaryLogisticRegression):

    @staticmethod
    def objective_function(w,X,y,C1,C2):
        g = expit(X @ w)
        # invert this because scipy minimizes, but we derived all formulas for maximzing
        return -np.sum(ma.log(g[y==1]))-np.sum(ma.log(1-g[y==0])) + C2*sum(w**2) + C1*sum(np.abs(w))
        #-np.sum(y*np.log(g)+(1-y)*np.log(1-g))

    @staticmethod
    def objective_gradient(w,X,y,C1, C2):
        g = expit(X @ w)
        ydiff = y-g
        gradient = np.mean(X * ydiff[:,np.newaxis], axis=0)
        gradient = gradient.reshape(w.shape)
        gradient[1:] += -2 * w[1:] * C2
        l1_der = w[1:] / np.abs(w[1:])
        l1_der[w[1:] == 0] = 0
        gradient[1:] +=  -1 * l1_der * C1

        return -gradient

    # just overwrite fit function
    def fit(self, X, y, regularization=None):
        Xb = self._add_bias(X) # add bias term
        num_samples, num_features = Xb.shape
        if(regularization == 'L1'):
            self.C2 = 0
        elif(regularization == 'L2'):
            self.C1 = 0
        elif(regularization == 'elastic'):
            pass
        else:
            self.C1 = 0
            self.C2 = 0

        self.w_ = fmin_bfgs(self.objective_function, # what to optimize
                            np.zeros((num_features,1)), # starting point
                            fprime=self.objective_gradient, # gradient function
                            args=(Xb,y,self.C1,self.C2), # extra args for gradient and objective function
                            gtol=1e-03, # stopping criteria for gradient, |v_k|
                            maxiter=self.iters, # stopping criteria iterations
                            disp=False)

        self.w_ = self.w_.reshape((num_features,1))

bfgslr = BFGSBinaryLogisticRegression(_,iterations=3,C1=1.000,C2=1.000) # note that we need only a few iterations here

bfgslr.fit(X_train,y_train,regularization=None)
yhat = bfgslr.predict(X_test)
#print(bfgslr)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.10034602076124567
CPU times: user 123 ms, sys: 198 ms, total: 321 ms
Wall time: 50.1 ms


bfgslr = BFGSBinaryLogisticRegression(_,iterations=3,C1=1.000,C2=1.000)
bfgslr.fit(X_train,y_train,regularization='L1')
yhat = bfgslr.predict(X_test)
#print(bfgslr)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.10034602076124567


bfgslr = BFGSBinaryLogisticRegression(_,iterations=3,C1=1.000,C2=1.000)
bfgslr.fit(X_train,y_train,regularization='L2')
yhat = bfgslr.predict(X_test)
#print(bfgslr)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.10034602076124567


bfgslr = BFGSBinaryLogisticRegression(_,iterations=3,C1=1.000,C2=1.000)
bfgslr.fit(X_train,y_train,regularization='elastic')
yhat = bfgslr.predict(X_test)
#print(bfgslr)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.11245674740484429


%%time
from numpy.linalg import pinv
class HessianBinaryLogisticRegression(BinaryLogisticRegression):
    # just overwrite gradient function
    def _get_gradient(self,X,y):
        g = self.predict_proba(X,add_bias=False).ravel() # get sigmoid value for all classes
        hessian = X.T @ np.diag(g*(1-g)) @ X - 2 * self.C2 # calculate the hessian

        ydiff = y-g # get y difference
        gradient = np.sum(X * ydiff[:,np.newaxis], axis=0) # make ydiff a column vector and multiply through
        gradient = gradient.reshape(self.w_.shape)
        gradient[1:] += -2 * self.w_[1:] * self.C2

        return pinv(hessian) @ gradient

hlr = HessianBinaryLogisticRegression(eta=1.0,
                                      iterations=4,
                                      C1=0.001,
                                      C2=0.001) # note that we need only a few iterations here

hlr.fit(X_train,y_train, regularization='elastic')
yhat = hlr.predict(X_test)
#print(hlr)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.09169550173010381
CPU times: user 824 ms, sys: 476 ms, total: 1.3 s
Wall time: 178 ms


# allow for the user to specify the algorithm they want to solver the binary case
class MultiClassLogisticRegression:
    def __init__(self, eta, iterations=20,
                 C1=0.0001,
                 C2=0.0001,
                 solver=BFGSBinaryLogisticRegression):
        self.eta = eta
        self.iters = iterations
        self.C1 = C1
        self.C2 = C2
        self.solver = solver
        self.classifiers_ = []
        # internally we will store the weights as self.w_ to keep with sklearn conventions

    def __str__(self):
        if(hasattr(self,'w_')):
            return 'MultiClass Logistic Regression Object with coefficients:\n'+ str(self.w_) # is we have trained the object
        else:
            return 'Untrained MultiClass Logistic Regression Object'

    def fit(self,X,y, regularization=None):
        num_samples, num_features = X.shape
        self.unique_ = np.sort(np.unique(y)) # get each unique class value
        num_unique_classes = len(self.unique_)
        self.classifiers_ = []
        for i,yval in enumerate(self.unique_): # for each unique value
            y_binary = np.array(y==yval).astype(int) # create a binary problem
            # train the binary classifier for this class

            hblr = self.solver(eta=self.eta,iterations=self.iters,
                               C1=self.C1,
                               C2=self.C2
                               )
            hblr.fit(X,y_binary, regularization=regularization)

            # add the trained classifier to the list
            self.classifiers_.append(hblr)

        # save all the weights into one matrix, separate column for each class
        self.w_ = np.hstack([x.w_ for x in self.classifiers_]).T

    def predict_proba(self,X):
        probs = []
        for hblr in self.classifiers_:
            probs.append(hblr.predict_proba(X).reshape((len(X),1))) # get probability for each classifier

        return np.hstack(probs) # make into single matrix

    def predict(self,X):
        return self.unique_[np.argmax(self.predict_proba(X),axis=1)] # take argmax along row


%%time
lr = MultiClassLogisticRegression(eta=1.0,
                                  iterations=4,
                                  C1=0.01,
                                  C2=0.01,
                                  solver=BFGSBinaryLogisticRegression
                                 )
lr.fit(X_train,y_train)
#print(lr)

yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.870242214532872
CPU times: user 109 ms, sys: 157 ms, total: 265 ms
Wall time: 51.9 ms


%%time
lr = MultiClassLogisticRegression(eta=1.0,
                                  iterations=4,
                                  C1=0.01,
                                  C2=0.01,
                                  solver=HessianBinaryLogisticRegression
                                 )
lr.fit(X_train,y_train,regularization=None)
#print(lr)

yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.9204152249134948
CPU times: user 4.13 s, sys: 2.53 s, total: 6.66 s
Wall time: 917 ms


%%time
lr = MultiClassLogisticRegression(eta=0.1,
                                  iterations=100,
                                  C1=0.01,
                                  C2=0.01,
                                  solver=StochasticLogisticRegression
                                 )
lr.fit(X_train,y_train,regularization="L1")
#print(lr)

yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.8823529411764706
CPU times: user 64.1 ms, sys: 9.8 ms, total: 73.9 ms
Wall time: 66.8 ms


%%time
lr = MultiClassLogisticRegression(eta=0.1,
                                  iterations=4,
                                  C1=0.01,
                                  C2=0.01,
                                  solver=VectorBinaryLogisticRegression
                                 )
lr.fit(X_train,y_train,regularization=None)
#print(lr)

yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.8806228373702422
CPU times: user 50.9 ms, sys: 85.9 ms, total: 137 ms
Wall time: 32.6 ms


param = {
    'iterations': [10, 100, 1000],
    'C1': [0.1, 0.01, 0.001, 0.0001],
    'C2': [0.1, 0.01, 0.001, 0.0001],
    'regularization': [None, "L1", "L2", "elastic"],
    'solver': [
        ("Quasi-Newton", BFGSBinaryLogisticRegression), # Quasi-Newton's method
        ("SGD", StochasticLogisticRegression),
        ("Gradient", VectorBinaryLogisticRegression) # Gradient Descent
        ],
    }

accuracies = []
param_accuracies = []

for (sn, s) in param['solver']:
  for c1 in param['C1']:
    for c2 in param['C2']:
      for r in param['regularization']:
        for it in param['iterations']:
          #%%time
          params = f"solver: {sn} Regularization: {r} C1: {c1} C2: {c2} iters: {it}"
          print(params)
          lr = MultiClassLogisticRegression(eta=0.1,
                                            iterations=it,
                                            C1=c1,
                                            C2=c2,
                                            solver=s
                                          )
          lr.fit(X_train,y_train)
          #print(lr)

          yhat = lr.predict(X_test)
          #print('Accuracy of: ',accuracy_score(y_test,yhat))

          acc = accuracy_score(y_test, yhat)
          accuracies.append(acc)
          param_accuracies.append(params)
          print('Accuracy: ', acc, params)

accuracies = np.array(accuracies)
param_accuracies = np.array(param_accuracies)
ind = np.argpartition(accuracies, -5)[-5:]

top5acc = accuracies[ind]
top5params = param_accuracies[ind]

solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.903114186851211 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100
solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.9359861591695502 solver: Quasi-Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000
solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.7439446366782007 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 10
solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.8719723183391004 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 100
solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.8944636678200693 solver: SGD Regularization: None C1: 0.1 C2: 0.1 iters: 1000
solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.7975778546712803 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 10
solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.8771626297577855 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 100
solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.884083044982699 solver: SGD Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000
solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.7370242214532872 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 10
solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.8581314878892734 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 100
solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.8944636678200693 solver: SGD Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000
solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.745674740484429 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 10
solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 100
solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.884083044982699 solver: SGD Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000
solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.7785467128027682 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 10
solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.8719723183391004 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 100
solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.8996539792387543 solver: SGD Regularization: None C1: 0.1 C2: 0.01 iters: 1000
solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.7698961937716263 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 10
solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.8598615916955017 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 100
solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.8961937716262975 solver: SGD Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000
solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.7889273356401384 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 10
solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.8615916955017301 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 100
solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.8823529411764706 solver: SGD Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000
solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.7422145328719724 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 10
solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.8494809688581315 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 100
solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.889273356401384 solver: SGD Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000
solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.7820069204152249 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 10
solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.8650519031141869 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 100
solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.889273356401384 solver: SGD Regularization: None C1: 0.1 C2: 0.001 iters: 1000
solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.7370242214532872 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 10
solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.856401384083045 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 100
solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.9013840830449827 solver: SGD Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000
solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.759515570934256 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 10
solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.8633217993079585 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 100
solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.8927335640138409 solver: SGD Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000
solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.773356401384083 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 10
solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.8598615916955017 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 100
solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.8910034602076125 solver: SGD Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000
solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.7647058823529411 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 10
solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.8512110726643599 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 100
solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.870242214532872 solver: SGD Regularization: None C1: 0.1 C2: 0.0001 iters: 1000
solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.7750865051903114 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10
solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.8615916955017301 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100
solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.8806228373702422 solver: SGD Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000
solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.7525951557093425 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10
solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.8442906574394463 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100
solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.8961937716262975 solver: SGD Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000
solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.7647058823529411 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10
solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.8615916955017301 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100
solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.8771626297577855 solver: SGD Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000
solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.7560553633217993 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 10
solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.8633217993079585 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 100
solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.8927335640138409 solver: SGD Regularization: None C1: 0.01 C2: 0.1 iters: 1000
solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.7698961937716263 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 10
solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.8771626297577855 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 100
solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.884083044982699 solver: SGD Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000
solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.745674740484429 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 10
solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.8667820069204152 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 100
solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.8927335640138409 solver: SGD Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000
solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.7820069204152249 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 10
solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.8598615916955017 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 100
solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.8788927335640139 solver: SGD Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000
solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.7785467128027682 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 10
solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.8650519031141869 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 100
solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.8788927335640139 solver: SGD Regularization: None C1: 0.01 C2: 0.01 iters: 1000
solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.759515570934256 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 10
solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 100
solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.8858131487889274 solver: SGD Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000
solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.745674740484429 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 10
solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.8581314878892734 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 100
solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.8910034602076125 solver: SGD Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000
solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.7577854671280276 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 10
solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.8737024221453287 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 100
solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.8754325259515571 solver: SGD Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000
solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.71280276816609 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 10
solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.8719723183391004 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 100
solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.8823529411764706 solver: SGD Regularization: None C1: 0.01 C2: 0.001 iters: 1000
solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.7577854671280276 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 10
solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.8685121107266436 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 100
solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.889273356401384 solver: SGD Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000
solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.7716262975778547 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 10
solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.8546712802768166 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 100
solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.8788927335640139 solver: SGD Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000
solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.7162629757785467 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 10
solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.8685121107266436 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 100
solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.8858131487889274 solver: SGD Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000
solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.7802768166089965 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 10
solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.8615916955017301 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 100
solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.8875432525951558 solver: SGD Regularization: None C1: 0.01 C2: 0.0001 iters: 1000
solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.7716262975778547 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10
solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100
solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.8858131487889274 solver: SGD Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000
solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.7923875432525952 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10
solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.8581314878892734 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100
solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.8806228373702422 solver: SGD Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000
solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.7889273356401384 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10
solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.8650519031141869 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100
solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.8754325259515571 solver: SGD Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000
solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.7768166089965398 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 10
solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.8667820069204152 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 100
solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.8771626297577855 solver: SGD Regularization: None C1: 0.001 C2: 0.1 iters: 1000
solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.7629757785467128 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 10
solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 100
solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.889273356401384 solver: SGD Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000
solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.7629757785467128 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 10
solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.8685121107266436 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 100
solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.8875432525951558 solver: SGD Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000
solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.7785467128027682 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 10
solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.8546712802768166 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 100
solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.8961937716262975 solver: SGD Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000
solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.745674740484429 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 10
solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.856401384083045 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 100
solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.8961937716262975 solver: SGD Regularization: None C1: 0.001 C2: 0.01 iters: 1000
solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.7612456747404844 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 10
solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.8719723183391004 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 100
solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.884083044982699 solver: SGD Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000
solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.7854671280276817 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 10
solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.8667820069204152 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 100
solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.8875432525951558 solver: SGD Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000
solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.7820069204152249 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 10
solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.8546712802768166 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 100
solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.889273356401384 solver: SGD Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000
solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.7854671280276817 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 10
solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.8615916955017301 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 100
solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.889273356401384 solver: SGD Regularization: None C1: 0.001 C2: 0.001 iters: 1000
solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.7370242214532872 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 10
solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.8667820069204152 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 100
solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.884083044982699 solver: SGD Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000
solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.7664359861591695 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 10
solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.8667820069204152 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 100
solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.884083044982699 solver: SGD Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000
solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.7802768166089965 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 10
solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.8615916955017301 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 100
solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.8823529411764706 solver: SGD Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000
solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.7577854671280276 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 10
solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.8650519031141869 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 100
solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.8823529411764706 solver: SGD Regularization: None C1: 0.001 C2: 0.0001 iters: 1000
solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.7577854671280276 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10
solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.8650519031141869 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100
solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.889273356401384 solver: SGD Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000
solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.7525951557093425 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10
solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.8633217993079585 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100
solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.8806228373702422 solver: SGD Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000
solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.7923875432525952 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10
solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.8771626297577855 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100
solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.8910034602076125 solver: SGD Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000
solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.7577854671280276 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 10
solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.8529411764705882 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 100
solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.8788927335640139 solver: SGD Regularization: None C1: 0.0001 C2: 0.1 iters: 1000
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.7629757785467128 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.8442906574394463 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.9013840830449827 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.7664359861591695 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.8650519031141869 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.8910034602076125 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.7387543252595156 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.8719723183391004 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.8875432525951558 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000
solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.7768166089965398 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 10
solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.8512110726643599 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 100
solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.8858131487889274 solver: SGD Regularization: None C1: 0.0001 C2: 0.01 iters: 1000
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.7820069204152249 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.8771626297577855 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.903114186851211 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.7785467128027682 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.8685121107266436 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.8944636678200693 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.7647058823529411 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.8581314878892734 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.8806228373702422 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000
solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.7577854671280276 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 10
solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.8615916955017301 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 100
solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.8875432525951558 solver: SGD Regularization: None C1: 0.0001 C2: 0.001 iters: 1000
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.7560553633217993 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.8685121107266436 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.8823529411764706 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.7629757785467128 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.8615916955017301 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.8823529411764706 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.7854671280276817 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.8771626297577855 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.8944636678200693 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000
solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.7629757785467128 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 10
solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.8442906574394463 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 100
solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.8944636678200693 solver: SGD Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.773356401384083 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.8529411764705882 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100
solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.884083044982699 solver: SGD Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.7197231833910035 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.8685121107266436 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100
solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.889273356401384 solver: SGD Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.773356401384083 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.8598615916955017 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100
solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.8961937716262975 solver: SGD Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000
solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 10
solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 100
solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.1 C2: 0.1 iters: 1000
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 10
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 100
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.1 iters: 1000
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 10
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 100
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.1 iters: 1000
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 10
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 100
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.1 iters: 1000
solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 10
solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 100
solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.1 C2: 0.01 iters: 1000
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 10
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 100
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.01 iters: 1000
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 10
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 100
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.01 iters: 1000
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 10
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 100
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.01 iters: 1000
solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 10
solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 100
solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.1 C2: 0.001 iters: 1000
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 10
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 100
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.001 iters: 1000
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 10
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 100
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.001 iters: 1000
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 10
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 100
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.001 iters: 1000
solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 10
solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 100
solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.1 C2: 0.0001 iters: 1000
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 100
solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1000
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 100
solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1000
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 100
solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1000
solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 10
solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 100
solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.01 C2: 0.1 iters: 1000
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 10
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 100
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.1 iters: 1000
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 10
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 100
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.1 iters: 1000
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 10
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 100
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.1 iters: 1000
solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 10
solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 100
solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.01 C2: 0.01 iters: 1000
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 10
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 100
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.01 iters: 1000
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 10
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 100
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.01 iters: 1000
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 10
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 100
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.01 iters: 1000
solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 10
solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 100
solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.01 C2: 0.001 iters: 1000
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 10
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 100
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.001 iters: 1000
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 10
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 100
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.001 iters: 1000
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 10
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 100
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.001 iters: 1000
solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 10
solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 100
solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.01 C2: 0.0001 iters: 1000
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 100
solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 100
solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1000
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100
solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000
solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 10
solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 100
solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.001 C2: 0.1 iters: 1000
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 10
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 100
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.1 iters: 1000
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 10
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 100
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.1 iters: 1000
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 10
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 100
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.1 iters: 1000
solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 10
solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 100
solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.001 C2: 0.01 iters: 1000
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 10
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 100
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.01 iters: 1000
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 10
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 100
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.01 iters: 1000
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 10
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 100
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.01 iters: 1000
solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 10
solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 100
solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.001 C2: 0.001 iters: 1000
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 10
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 100
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.001 iters: 1000
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 10
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 100
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.001 iters: 1000
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 10
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 100
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.001 iters: 1000
solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 10
solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 100
solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.001 C2: 0.0001 iters: 1000
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 100
solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1000
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 100
solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1000
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 100
solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1000
solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 10
solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 100
solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.0001 C2: 0.1 iters: 1000
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 100
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1000
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 100
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1000
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 100
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1000
solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 10
solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 100
solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.0001 C2: 0.01 iters: 1000
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 100
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1000
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1000
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 100
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1000
solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 10
solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 100
solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.0001 C2: 0.001 iters: 1000
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 100
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1000
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 100
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1000
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 100
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1000
solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 10
solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 100
solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: None C1: 0.0001 C2: 0.0001 iters: 1000
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 100
solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1000
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 100
solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1000
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.884083044982699 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100
Accuracy:  0.870242214532872 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 100
solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000
Accuracy:  0.8979238754325259 solver: Gradient Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1000


print(top5acc)
print(top5params)

[0.93598616 0.93598616 0.93598616 0.93598616 0.93598616]
['solver: Quasi-Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 100'
 'solver: Quasi-Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1000'
 'solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1000'
 'solver: Quasi-Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 100'
 'solver: Quasi-Newton Regularization: None C1: 0.01 C2: 0.001 iters: 100']


param = {
    'iterations': [1, 4, 10],
    'C1': [0.1, 0.01, 0.001, 0.0001],
    'C2': [0.1, 0.01, 0.001, 0.0001],
    'regularization': [None, "L1", "L2", "elastic"],
    'solver': [
        ("Newton", HessianBinaryLogisticRegression), # Newton's method
        ],
    }

accuracies = []
param_accuracies = []

for (sn, s) in param['solver']:
  for c1 in param['C1']:
    for c2 in param['C2']:
      for r in param['regularization']:
        for it in param['iterations']:
          #%%time
          params = f"solver: {sn} Regularization: {r} C1: {c1} C2: {c2} iters: {it}"
          print(params)
          lr = MultiClassLogisticRegression(eta=0.1,
                                            iterations=it,
                                            C1=c1,
                                            C2=c2,
                                            solver=s
                                          )
          lr.fit(X_train,y_train)
          #print(lr)

          yhat = lr.predict(X_test)
          #print('Accuracy of: ',accuracy_score(y_test,yhat))

          acc = accuracy_score(y_test, yhat)
          accuracies.append(acc)
          param_accuracies.append(params)
          print('Accuracy: ', acc, params)

accuracies = np.array(accuracies)
param_accuracies = np.array(param_accuracies)
ind = np.argpartition(accuracies, -5)[-5:]

top5acc = accuracies[ind]
top5params = param_accuracies[ind]

solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 1
solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 4
solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.1 C2: 0.1 iters: 10
solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 1
solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 4
solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.1 C2: 0.1 iters: 10
solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 1
solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 4
solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.1 C2: 0.1 iters: 10
solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 1
solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 4
solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.1 C2: 0.1 iters: 10
solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 1
solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 4
solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.1 C2: 0.01 iters: 10
solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 1
solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 4
solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.1 C2: 0.01 iters: 10
solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 1
solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 4
solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.1 C2: 0.01 iters: 10
solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 1
solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 4
solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.1 C2: 0.01 iters: 10
solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 1
solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 4
solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.1 C2: 0.001 iters: 10
solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 1
solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 4
solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.1 C2: 0.001 iters: 10
solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 1
solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 4
solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.1 C2: 0.001 iters: 10
solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 1
solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 4
solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.1 C2: 0.001 iters: 10
solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 1
solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 4
solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.1 C2: 0.0001 iters: 10
solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 1
solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 4
solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.1 C2: 0.0001 iters: 10
solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 1
solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 4
solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.1 C2: 0.0001 iters: 10
solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 1
solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 4
solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.1 C2: 0.0001 iters: 10
solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 1
solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 4
solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.01 C2: 0.1 iters: 10
solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 1
solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 4
solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.01 C2: 0.1 iters: 10
solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 1
solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 4
solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.01 C2: 0.1 iters: 10
solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 1
solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 4
solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.01 C2: 0.1 iters: 10
solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 1
solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 4
solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.01 C2: 0.01 iters: 10
solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 1
solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 4
solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.01 C2: 0.01 iters: 10
solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 1
solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 4
solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.01 C2: 0.01 iters: 10
solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 1
solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 4
solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.01 C2: 0.01 iters: 10
solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 1
solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 4
solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.01 C2: 0.001 iters: 10
solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 1
solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 4
solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.01 C2: 0.001 iters: 10
solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 1
solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 4
solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.01 C2: 0.001 iters: 10
solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 1
solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 4
solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.01 C2: 0.001 iters: 10
solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 1
solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 4
solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.01 C2: 0.0001 iters: 10
solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 1
solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 4
solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.01 C2: 0.0001 iters: 10
solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 1
solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 4
solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10
solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 1
solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 4
solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.01 C2: 0.0001 iters: 10
solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 1
solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 4
solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10
solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 1
solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 4
solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10
solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 1
solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 4
solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10
solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 1
solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 4
solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.001 C2: 0.1 iters: 10
solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 1
solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 4
solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.001 C2: 0.01 iters: 10
solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 1
solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 4
solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.001 C2: 0.01 iters: 10
solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 1
solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 4
solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.001 C2: 0.01 iters: 10
solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 1
solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 4
solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.001 C2: 0.01 iters: 10
solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 1
solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 4
solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.001 C2: 0.001 iters: 10
solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 1
solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 4
solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.001 C2: 0.001 iters: 10
solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 1
solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 4
solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.001 C2: 0.001 iters: 10
solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 1
solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 4
solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.001 C2: 0.001 iters: 10
solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 1
solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 4
solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.001 C2: 0.0001 iters: 10
solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 1
solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 4
solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.001 C2: 0.0001 iters: 10
solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 1
solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 4
solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.001 C2: 0.0001 iters: 10
solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 1
solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 4
solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.001 C2: 0.0001 iters: 10
solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 1
solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 4
solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.0001 C2: 0.1 iters: 10
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 1
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 4
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.1 iters: 10
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 1
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 4
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.1 iters: 10
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 1
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 4
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.1 iters: 10
solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 1
solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 4
solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.0001 C2: 0.01 iters: 10
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 1
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 4
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.01 iters: 10
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 1
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 4
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.01 iters: 10
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 1
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 4
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.01 iters: 10
solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 1
solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 4
solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.0001 C2: 0.001 iters: 10
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 1
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 4
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.001 iters: 10
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 1
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 4
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.001 iters: 10
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 1
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 4
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.001 iters: 10
solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 1
solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 4
solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: None C1: 0.0001 C2: 0.0001 iters: 10
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 1
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 4
solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L1 C1: 0.0001 C2: 0.0001 iters: 10
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 1
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 4
solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: L2 C1: 0.0001 C2: 0.0001 iters: 10
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1
Accuracy:  0.8719723183391004 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 1
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 4
Accuracy:  0.8737024221453287 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 4
solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10
Accuracy:  0.8806228373702422 solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10


print(top5acc)
print(top5params)

[0.88062284 0.88062284 0.88062284 0.88062284 0.88062284]
['solver: Newton Regularization: L1 C1: 0.001 C2: 0.1 iters: 10'
 'solver: Newton Regularization: None C1: 0.001 C2: 0.1 iters: 10'
 'solver: Newton Regularization: L2 C1: 0.001 C2: 0.1 iters: 10'
 'solver: Newton Regularization: L2 C1: 0.01 C2: 0.0001 iters: 10'
 'solver: Newton Regularization: elastic C1: 0.0001 C2: 0.0001 iters: 10']


import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly
import plotly.io as pio

iter = [2, 4, 8, 16 ,32, 128, 256, 512, 1024]
iter_accur = []

for i in iter:
    lr = MultiClassLogisticRegression(eta=0.1,
                                  iterations= i,
                                  C1=0.01,
                                  C2=0.01,
                                  solver=BFGSBinaryLogisticRegression
                                 )
    lr.fit(X_train,y_train)
    y_hat_new = lr.predict(X_test)
    iter_accur.append(accuracy_score(y_test, y_hat_new))

# using plotly
fig = go.Figure(data=go.Scatter(x=iter, y=iter_accur))
fig.update_layout(title_text='Quasi Newton - Accuracy Vs Number of Iterations Quasi Newton', xaxis_title="Number of Iterations", yaxis_title="Accuracy")
fig.show()


#print(lr)


c1 = [.0001, .001, .01, .1, 1]
c2 = [.0001, .001, .01, .1, 1]
C_accur = []
for i, j in zip (c1, c2):
    lr = MultiClassLogisticRegression(eta=0.01,
                                  iterations=10,
                                  C1= i,
                                  C2= j ,
                                  solver=BFGSBinaryLogisticRegression
                                 )
    lr.fit(X_train,y_train, regularization = "elastic")
    y_hat_new = lr.predict(X_test)
    C_accur.append(accuracy_score(y_test, y_hat_new))


# using plotly
fig = go.Figure(data=go.Scatter(x=c1, y=C_accur))
fig.update_layout(title_text='Quasi Newton - Accuracy Vs Regularization Strength', xaxis_title="Regularization Strength (ElasticNet)", yaxis_title="Accuracy")
fig.show()


import time 
optimizations = [
        BFGSBinaryLogisticRegression, # Quasi-Newton's method
        StochasticLogisticRegression,
        VectorBinaryLogisticRegression,
        HessianBinaryLogisticRegression
        # Gradient Descent
        ]

accur= []
times = []
for i in optimizations:
    print(i)
    model = MultiClassLogisticRegression(eta=0.1,
                                  iterations= 10,
                                  C1=0,
                                  C2=0,
                                  solver= i
                                 )
    
    start = time.time()
    model.fit(X_train, y_train)
    end = time.time()
    y_hat_new = model.predict(X_test)
    accur.append(accuracy_score(y_test, y_hat_new))
    times.append(end-start)
    print(end-start)

<class '__main__.BFGSBinaryLogisticRegression'>
0.10863828659057617
<class '__main__.StochasticLogisticRegression'>
0.0032608509063720703
<class '__main__.VectorBinaryLogisticRegression'>
0.022936105728149414
<class '__main__.HessianBinaryLogisticRegression'>
2.4437451362609863


print(accur)
print(times)

[0.903114186851211, 0.7854671280276817, 0.884083044982699, 0.8806228373702422]
[0.10863828659057617, 0.0032608509063720703, 0.022936105728149414, 2.4437451362609863]


%%time
lr = MultiClassLogisticRegression(eta=0.01,
                                  iterations=1000,
                                  C1=0.01,
                                  C2=0.01,
                                  solver=BFGSBinaryLogisticRegression
                                 )
lr.fit(X_train,y_train)
#print(lr)

yhat = lr.predict(X_test)
print('Accuracy of: ',accuracy_score(y_test,yhat))

Accuracy of:  0.9359861591695502
CPU times: user 1.4 s, sys: 1.54 s, total: 2.94 s
Wall time: 424 ms


%%time
from sklearn.linear_model import LogisticRegression as SKLogisticRegression

sci_kit_model = SKLogisticRegression(solver='lbfgs', max_iter=1000, multi_class='ovr', penalty = "none")
sci_kit_model.fit(X_train, y_train)
print(accuracy_score(y_true=y_test, y_pred=sci_kit_model.predict(X_test)))

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

0.9377162629757786
CPU times: user 5.78 s, sys: 5.89 s, total: 11.7 s
Wall time: 1.62 s


%%time
from sklearn.linear_model import LogisticRegression as SKLogisticRegression

sci_kit_model = SKLogisticRegression(solver='lbfgs', max_iter=1000, penalty = "none")
sci_kit_model.fit(X_train, y_train)
print(accuracy_score(y_true=y_test, y_pred=sci_kit_model.predict(X_test)))

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

0.9653979238754326
CPU times: user 2.96 s, sys: 3.32 s, total: 6.27 s
Wall time: 863 ms

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression


import time
iter = [2, 4, 8, 16 ,32, 128, 256, 512, 1024]
iter_accur = []
sci_kit_accur = [] 
time_BFGS = [] 
time_SKLEARN = []


for i in iter:
    lr = MultiClassLogisticRegression(eta=0.1,
                                  iterations= i,
                                  C1=0.01,
                                  C2=0.01,
                                  solver=BFGSBinaryLogisticRegression
                                 )
    sci_kit_model = SKLogisticRegression(solver='lbfgs', max_iter=i, penalty = "none")
    start1 = time.time()
    sci_kit_model.fit(X_train, y_train)
    end1 = time.time()

    start2 =time.time()
    lr.fit(X_train,y_train)
    end2 = time.time()
    y_hat_new = lr.predict(X_test)
    y_hat_sk = sci_kit_model.predict(X_test)
    iter_accur.append(accuracy_score(y_test, y_hat_new))
    time_BFGS.append(end2- start2)
    sci_kit_accur.append(accuracy_score(y_test, y_hat_sk))
    time_SKLEARN.append(end1- start1) 

# using plotly
# trace1 = go.Scatter(
#     x = iter,
#     y = iter_accur
# )

# trace2 = go.Scatter(
#     x = iter,
#     y = sci_kit_accur
    
# )

# data = [trace1, trace2]

# fig = go.Figure(data=data )

# fig.update_layout(title_text='Accuracy Vs Number of Iterations Quasi Newton', xaxis_title="Number of Iterations", yaxis_title="Accuracy")
# fig.show()

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:1182: FutureWarning:

`penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.

/Users/AbhilashArnipalli/anaconda3/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression


trace1 = go.Scatter(
    x = np.cumsum(time_BFGS),
    y = iter_accur
)



data = [trace1]

fig = go.Figure(data=data )

fig.update_layout(title_text='Custom Implementation Accuracy Vs Time', xaxis_title="Time (seconds)", yaxis_title="Accuracy")
fig.show()


trace2 = go.Scatter(
    x = np.cumsum(time_SKLEARN),
    y = sci_kit_accur
)



data = [trace2]

fig = go.Figure(data=data )

fig.update_layout(title_text='SKLearn Accuracy Vs Time', xaxis_title="Time (seconds)", yaxis_title="Accuracy")
fig.show()


from itertools import product
etas = range(1, 10, 1)
cs = [0.001, 0.005, 0.015, 0.1, .15]
iterations = [25, 50,75,100,125, 150]
penalties = ['none', 'L1', 'L2', 'L1L2']

for penalty, c, itrs, eta in product(penalties, cs, iterations, etas):
    lr = MultiClassLogisticRegression(eta=eta/100,
                                  iterations= itrs,
                                  C1=c,
                                  C2=c,
                                  solver=VectorBinaryLogisticRegression
                                 )
    lr.fit(X_train, y_train)
    yhat = lr.predict(X_test)
    print(f'When Penalty: {penalty} C: {c}, with Eta: {eta/100}, for Stochastic Logistic Regression, where iters: {itrs} Accuracy of: {accuracy_score(y_test, yhat)}')

When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: none C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: none C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: none C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: none C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.001, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.001, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.001, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.001, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.005, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.005, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.005, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.005, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.015, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.015, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.015, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.015, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.1, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.1, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.1, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.1, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004
When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 25 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8823529411764706
When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 50 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 75 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.884083044982699
When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8806228373702422
When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 100 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8771626297577855
When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 125 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.01, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8788927335640139
When Penalty: L1L2 C: 0.15, with Eta: 0.02, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8737024221453287
When Penalty: L1L2 C: 0.15, with Eta: 0.03, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.04, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.05, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8685121107266436
When Penalty: L1L2 C: 0.15, with Eta: 0.06, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.07, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.08, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.870242214532872
When Penalty: L1L2 C: 0.15, with Eta: 0.09, for Stochastic Logistic Regression, where iters: 150 Accuracy of: 0.8719723183391004

	Entity	Year	Access to electricity (% of population)	Access to clean fuels for cooking	Renewable-electricity-generating-capacity-per-capita	Financial flows to developing countries (US $)	Renewable energy share in the total final energy consumption (%)	Electricity from fossil fuels (TWh)	Electricity from nuclear (TWh)	Electricity from renewables (TWh)	...	Primary energy consumption per capita (kWh/person)	Energy intensity level of primary energy (MJ/$2017 PPP GDP)	Value_co2_emissions_kt_by_country	Renewables (% equivalent primary energy)	gdp_growth	gdp_per_capita	Density\n(P/Km2)	Land Area(Km2)	Latitude	Longitude
0	Afghanistan	2000	1.613591	6.2	9.22	20000.0	44.99	0.16	0.0	0.31	...	302.59482	1.64	760.000000	NaN	NaN	NaN	60	652230.0	33.939110	67.709953
1	Afghanistan	2001	4.074574	7.2	8.86	130000.0	45.60	0.09	0.0	0.50	...	236.89185	1.74	730.000000	NaN	NaN	NaN	60	652230.0	33.939110	67.709953
2	Afghanistan	2002	9.409158	8.2	8.47	3950000.0	37.83	0.13	0.0	0.56	...	210.86215	1.40	1029.999971	NaN	NaN	179.426579	60	652230.0	33.939110	67.709953
3	Afghanistan	2003	14.738506	9.5	8.09	25970000.0	36.66	0.31	0.0	0.63	...	229.96822	1.40	1220.000029	NaN	8.832278	190.683814	60	652230.0	33.939110	67.709953
4	Afghanistan	2004	20.064968	10.9	7.75	NaN	44.24	0.33	0.0	0.56	...	204.23125	1.20	1029.999971	NaN	1.414118	211.382074	60	652230.0	33.939110	67.709953
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3644	Zimbabwe	2016	42.561730	29.8	62.88	30000.0	81.90	3.50	0.0	3.32	...	3227.68020	10.00	11020.000460	NaN	0.755869	1464.588957	38	390757.0	-19.015438	29.154857
3645	Zimbabwe	2017	44.178635	29.8	62.33	5570000.0	82.46	3.05	0.0	4.30	...	3068.01150	9.51	10340.000150	NaN	4.709492	1235.189032	38	390757.0	-19.015438	29.154857
3646	Zimbabwe	2018	45.572647	29.9	82.53	10000.0	80.23	3.73	0.0	5.46	...	3441.98580	9.83	12380.000110	NaN	4.824211	1254.642265	38	390757.0	-19.015438	29.154857
3647	Zimbabwe	2019	46.781475	30.1	81.40	250000.0	81.50	3.66	0.0	4.58	...	3003.65530	10.47	11760.000230	NaN	-6.144236	1316.740657	38	390757.0	-19.015438	29.154857
3648	Zimbabwe	2020	52.747670	30.4	80.61	30000.0	81.90	3.40	0.0	4.19	...	2680.13180	10.00	NaN	NaN	-6.248748	1214.509820	38	390757.0	-19.015438	29.154857

	Year	Access to clean fuels for cooking	Electricity from fossil fuels (TWh)	Electricity from nuclear (TWh)	Electricity from renewables (TWh)	Low-carbon electricity (% electricity)	Primary energy consumption per capita (kWh/person)	Energy intensity level of primary energy (MJ/$2017 PPP GDP)	Value_co2_emissions_kt_by_country	gdp_growth	...	Entity_Uganda	Entity_Ukraine	Entity_United Arab Emirates	Entity_United Kingdom	Entity_United States	Entity_Uruguay	Entity_Uzbekistan	Entity_Vanuatu	Entity_Zambia	Entity_Zimbabwe
count	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	...	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03	2.888000e+03
mean	1.511380e-14	-2.952394e-17	-9.841312e-18	-2.460328e-17	1.476197e-17	6.396853e-17	4.920656e-17	2.165089e-16	-3.444459e-17	8.857181e-17	...	-1.476197e-17	-4.428590e-17	-3.444459e-17	-2.460328e-17	-2.460328e-17	-7.380984e-17	-7.380984e-17	-5.412722e-17	-5.412722e-17	-2.460328e-17
std	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	...	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00	1.000173e+00
min	-1.670741e+00	-1.589617e+00	-2.041295e-01	-1.940244e-01	-2.466368e-01	-1.154582e+00	-6.996626e-01	-1.224983e+00	-2.056651e-01	-8.755782e+00	...	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02
25%	-8.011343e-01	-1.022023e+00	-2.033790e-01	-1.940244e-01	-2.457977e-01	-9.742272e-01	-6.263597e-01	-5.974468e-01	-2.029938e-01	-4.576486e-01	...	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02
50%	6.847250e-02	4.749434e-01	-1.961694e-01	-1.940244e-01	-2.303217e-01	-1.350514e-01	-3.828887e-01	-2.864800e-01	-1.932135e-01	-2.345189e-02	...	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02
75%	9.380793e-01	9.671142e-01	-1.360866e-01	-1.940244e-01	-1.535012e-01	8.129803e-01	1.537978e-01	2.009814e-01	-1.348503e-01	4.442603e-01	...	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02	-8.350749e-02
max	1.633765e+00	9.671142e-01	1.345998e+01	1.011700e+01	1.853496e+01	1.729051e+00	6.397726e+00	7.610956e+00	1.292949e+01	1.284183e+01	...	1.197497e+01	1.197497e+01	1.197497e+01	1.197497e+01	1.197497e+01	1.197497e+01	1.197497e+01	1.197497e+01	1.197497e+01	1.197497e+01

Lab Three: Extending Logistic Regression¶

Overview¶

Overview of the dataset¶

Features¶

Business Case:¶

Task Classification:¶

Data Preparation¶

Transforming my target variable¶

Training and Testing Split¶

Break down of the variables after pre processing¶

Custom Logistic Regression Class¶

Visualizing models with different iterations for Quasi Newton Solver¶

Visualizing Models and Comparing Accuracy and Runtime¶

Comparing my best model to sklearn's logistic regression¶

Comparing my best logistic regression optimization to SKlearn¶

Deployment¶

Conclusion¶

Additional Analysis¶